Markov Chain Monte Carlo (MCMC) algorithms have made a significant impact in the history of computing [1]}. MCMC methods are among the most powerful randomized algorithms with a wide range of applications in Artificial Intelligence (AI) [2]}. Powerful MCMC methods such as Metropolis and Gibbs sampling have been widely applied to training generative neural networks [3]}, probabilistic inference in belief networks [4]}, calculating physical observables in classical and quantum systems [5]}, [6]} and solving computationally hard combinatorial optimization problems [7]}.
